A Survey on Network Optimization Problem: Transport Network Design Problem

2018 ◽  
Author(s):  
Ruchita Singh Tomar ◽  
Shivi Agrawal
2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Sweta Srivastava ◽  
Sudip Kumar Sahana

The requirement of the road services and transportation network development planning came into existence with the development of civilization. In the modern urban transport scenario with the forever mounting amount of vehicles, it is very much essential to tackle network congestion and to minimize the travel time. This work is based on determining the optimal wait time at traffic signals for the microscopic discrete model. The problem is formulated as a bilevel model. The upper layer optimizes the travel time by reducing the wait time at traffic signal and the lower layer solves the stochastic user equilibrium. Soft computing techniques like Genetic Algorithms, Ant Colony Optimization, and many other biologically inspired techniques prove to give good results for bilevel problems. Here this work uses Bat Intelligence to solve the transport network design problem. The results are compared with the existing techniques.


2020 ◽  
Vol 284 (1) ◽  
pp. 188-200 ◽  
Author(s):  
Pirmin Fontaine ◽  
Teodor Gabriel Crainic ◽  
Michel Gendreau ◽  
Stefan Minner

2011 ◽  
Vol 31 (6) ◽  
pp. 743-768 ◽  
Author(s):  
Anthony Chen ◽  
Zhong Zhou ◽  
Piya Chootinan ◽  
Seungkyu Ryu ◽  
Chao Yang ◽  
...  

2021 ◽  
Author(s):  
Ovidiu Cosma ◽  
Petrică C Pop ◽  
Cosmin Sabo

Abstract In this paper we investigate a particular two-stage supply chain network design problem with fixed costs. In order to solve this complex optimization problem, we propose an efficient hybrid algorithm, which was obtained by incorporating a linear programming optimization problem within the framework of a genetic algorithm. In addition, we integrated within our proposed algorithm a powerful local search procedure able to perform a fine tuning of the global search. We evaluate our proposed solution approach on a set of large size instances. The achieved computational results prove the efficiency of our hybrid genetic algorithm in providing high-quality solutions within reasonable running-times and its superiority against other existing methods from the literature.


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